# coding: utf-8
# Copyright (c) 2023 Ant Group and its affiliates.

# modified from https://github.com/SwinTransformer/Video-Swin-Transformer\
# /blob/master/mmaction/models/backbones/swin_transformer.py

import math
import warnings
from functools import reduce, lru_cache
from operator import mul

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    def norm_cdf(x):
        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
            "The distribution of values may be incorrect.",
            stacklevel=2,
        )

    with torch.no_grad():
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)

        tensor.uniform_(2 * l - 1, 2 * u - 1)

        tensor.erfinv_()

        tensor.mul_(std * math.sqrt(2.0))
        tensor.add_(mean)

        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


def drop_path(x, drop_prob: float = 0.0, training: bool = False):
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Mlp(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    B, D, H, W, C = x.shape
    x = x.view(
        B,
        D // window_size[0],
        window_size[0],
        H // window_size[1],
        window_size[1],
        W // window_size[2],
        window_size[2],
        C,
    )
    windows = (
        x.permute(0, 1, 3, 5, 2, 4, 6, 7)
        .contiguous()
        .view(-1, reduce(mul, window_size), C)
    )
    return windows


def window_reverse(windows, window_size, B, D, H, W):
    x = windows.view(
        B,
        D // window_size[0],
        H // window_size[1],
        W // window_size[2],
        window_size[0],
        window_size[1],
        window_size[2],
        -1,
    )
    x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1)
    return x


def get_window_size(x_size, window_size, shift_size=None):
    use_window_size = list(window_size)
    if shift_size is not None:
        use_shift_size = list(shift_size)
    for i in range(len(x_size)):
        if x_size[i] <= window_size[i]:
            use_window_size[i] = x_size[i]
            if shift_size is not None:
                use_shift_size[i] = 0

    if shift_size is None:
        return tuple(use_window_size)
    else:
        return tuple(use_window_size), tuple(use_shift_size)


class WindowAttention3D(nn.Module):
    def __init__(
        self,
        dim,
        window_size,
        num_heads,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
    ):
        super().__init__()
        self.dim = dim
        self.window_size = window_size
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.relative_position_bias_table = nn.Parameter(
            torch.zeros(
                (2 * window_size[0] - 1)
                * (2 * window_size[1] - 1)
                * (2 * window_size[2] - 1),
                num_heads,
            )
        )

        coords_d = torch.arange(self.window_size[0])
        coords_h = torch.arange(self.window_size[1])
        coords_w = torch.arange(self.window_size[2])
        coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))
        coords_flatten = torch.flatten(coords, 1)
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()
        relative_coords[:, :, 0] += self.window_size[0] - 1
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 2] += self.window_size[2] - 1

        relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (
            2 * self.window_size[2] - 1
        )
        relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1
        relative_position_index = relative_coords.sum(-1)
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=0.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        B_, N, C = x.shape
        qkv = (
            self.qkv(x)
            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k, v = qkv[0], qkv[1], qkv[2]

        q = q * self.scale
        attn = q @ k.transpose(-2, -1)

        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index[:N, :N].reshape(-1)
        ].reshape(N, N, -1)
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
                1
            ).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class SwinTransformerBlock3D(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        window_size=(2, 7, 7),
        shift_size=(0, 0, 0),
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        use_checkpoint=False,
    ):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.use_checkpoint = use_checkpoint

        assert (
            0 <= self.shift_size[0] < self.window_size[0]
        ), "shift_size must in 0-window_size"
        assert (
            0 <= self.shift_size[1] < self.window_size[1]
        ), "shift_size must in 0-window_size"
        assert (
            0 <= self.shift_size[2] < self.window_size[2]
        ), "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention3D(
            dim,
            window_size=self.window_size,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

    def forward_part1(self, x, mask_matrix):
        B, D, H, W, C = x.shape
        window_size, shift_size = get_window_size(
            (D, H, W), self.window_size, self.shift_size
        )

        x = self.norm1(x)

        pad_l = pad_t = pad_d0 = 0
        pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0]
        pad_b = (window_size[1] - H % window_size[1]) % window_size[1]
        pad_r = (window_size[2] - W % window_size[2]) % window_size[2]
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
        _, Dp, Hp, Wp, _ = x.shape

        if any(i > 0 for i in shift_size):
            shifted_x = torch.roll(
                x,
                shifts=(-shift_size[0], -shift_size[1], -shift_size[2]),
                dims=(1, 2, 3),
            )
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None

        x_windows = window_partition(shifted_x, window_size)

        attn_windows = self.attn(x_windows, mask=attn_mask)

        attn_windows = attn_windows.view(-1, *(window_size + (C,)))
        shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp, Wp)

        if any(i > 0 for i in shift_size):
            x = torch.roll(
                shifted_x,
                shifts=(shift_size[0], shift_size[1], shift_size[2]),
                dims=(1, 2, 3),
            )
        else:
            x = shifted_x

        if pad_d1 > 0 or pad_r > 0 or pad_b > 0:
            x = x[:, :D, :H, :W, :].contiguous()
        return x

    def forward_part2(self, x):
        return self.drop_path(self.mlp(self.norm2(x)))

    def forward(self, x, mask_matrix):
        shortcut = x
        if self.use_checkpoint:
            x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
        else:
            x = self.forward_part1(x, mask_matrix)
        x = shortcut + self.drop_path(x)

        if self.use_checkpoint:
            x = x + checkpoint.checkpoint(self.forward_part2, x)
        else:
            x = x + self.forward_part2(x)

        return x


class PatchMerging(nn.Module):
    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        B, D, H, W, C = x.shape

        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        x0 = x[:, :, 0::2, 0::2, :]
        x1 = x[:, :, 1::2, 0::2, :]
        x2 = x[:, :, 0::2, 1::2, :]
        x3 = x[:, :, 1::2, 1::2, :]
        x = torch.cat([x0, x1, x2, x3], -1)

        x = self.norm(x)
        x = self.reduction(x)

        return x


@lru_cache()
def compute_mask(D, H, W, window_size, shift_size, device):
    img_mask = torch.zeros((1, D, H, W, 1), device=device)
    cnt = 0
    for d in (
        slice(-window_size[0]),
        slice(-window_size[0], -shift_size[0]),
        slice(-shift_size[0], None),
    ):
        for h in (
            slice(-window_size[1]),
            slice(-window_size[1], -shift_size[1]),
            slice(-shift_size[1], None),
        ):
            for w in (
                slice(-window_size[2]),
                slice(-window_size[2], -shift_size[2]),
                slice(-shift_size[2], None),
            ):
                img_mask[:, d, h, w, :] = cnt
                cnt += 1
    mask_windows = window_partition(img_mask, window_size)
    mask_windows = mask_windows.squeeze(-1)
    attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
        attn_mask == 0, float(0.0)
    )
    return attn_mask


class BasicLayer(nn.Module):
    def __init__(
        self,
        dim,
        depth,
        num_heads,
        window_size=(1, 7, 7),
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        downsample=None,
        use_checkpoint=False,
    ):
        super().__init__()
        self.window_size = window_size
        self.shift_size = tuple(i // 2 for i in window_size)
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        self.blocks = nn.ModuleList(
            [
                SwinTransformerBlock3D(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=(0, 0, 0) if (i % 2 == 0) else self.shift_size,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=drop_path[i]
                    if isinstance(drop_path, list)
                    else drop_path,
                    norm_layer=norm_layer,
                    use_checkpoint=use_checkpoint,
                )
                for i in range(depth)
            ]
        )

        self.downsample = downsample
        if self.downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)

    def forward(self, x):
        B, C, D, H, W = x.shape
        window_size, shift_size = get_window_size(
            (D, H, W), self.window_size, self.shift_size
        )
        x = x.permute(0, 2, 3, 4, 1)
        Dp = int(np.ceil(D / window_size[0])) * window_size[0]
        Hp = int(np.ceil(H / window_size[1])) * window_size[1]
        Wp = int(np.ceil(W / window_size[2])) * window_size[2]
        attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device)
        for blk in self.blocks:
            x = blk(x, attn_mask)
        x = x.view(B, D, H, W, -1)

        if self.downsample is not None:
            x = self.downsample(x)
        x = x.permute(0, 4, 1, 2, 3)
        return x


class PatchEmbed3D(nn.Module):
    def __init__(self, patch_size=(2, 4, 4), in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        self.patch_size = patch_size

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv3d(
            in_chans, embed_dim, kernel_size=patch_size, stride=(1, 4, 4)
        )
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        _, _, D, H, W = x.size()
        if W % self.patch_size[2] != 0:
            x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
        if H % self.patch_size[1] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
        x = F.pad(x, (0, 0, 0, 0, 0, 1))

        x = self.proj(x)
        if self.norm is not None:
            D, Wh, Ww = x.size(2), x.size(3), x.size(4)
            x = x.flatten(2).transpose(1, 2)
            x = self.norm(x)
            x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)

        return x


# model download path
# weights transffered from:
# [1] https://drive.google.com/file/d/1B1tkA9EnlQlK72xB8liz_WRo7WTEpJDt/view
# [2] https://github.com/tsujuifu/pytorch_violet
# [3] https://github.com/SwinTransformer/Video-Swin-Transformer/blob/\
# master/mmaction/models/backbones/swin_transformer.py
url_map = {
    "videoswin3D": "YourUrl/ckpt_video-swin.pt",
}


class SwinTransformer3D(nn.Module):
    def __init__(
        self,
        pretrained=None,
        pretrained2d=True,
        patch_size=(2, 4, 4),
        in_chans=3,
        embed_dim=96,
        depths=[2, 2, 18, 2],
        num_heads=[3, 6, 12, 24],
        window_size=(8, 7, 7),
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.2,
        norm_layer=nn.LayerNorm,
        patch_norm=True,
        frozen_stages=-1,
        use_checkpoint=None,
    ):
        super().__init__()

        self.pretrained = pretrained
        self.pretrained2d = pretrained2d
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm
        self.frozen_stages = frozen_stages
        self.window_size = window_size
        self.patch_size = patch_size

        self.patch_embed = PatchEmbed3D(
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
        )

        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]

        if use_checkpoint is None:
            use_checkpoint = False
        if not isinstance(use_checkpoint, (tuple, list)):
            use_checkpoint = [use_checkpoint] * self.num_layers

        self.layers = nn.ModuleList()

        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2**i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if i_layer < self.num_layers - 1 else None,
                use_checkpoint=use_checkpoint[i_layer],
            )
            self.layers.append(layer)

        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.norm = norm_layer(self.num_features)

    @classmethod
    def from_pretrained(cls, weights_path=None, pretrained=True, **kwargs):
        """create an efficientnet model according to name.

        Args:
            model_name (str): Name of PVT model, e.g. pvt_v2_b0
            weights_path (None or str):
                str: path to pretrained weights file on the local disk.
                None: use pretrained weights downloaded from the Internet.

        Returns:
            A pretrained pvt model.
        """
        model = SwinTransformer3D(**kwargs)

        if pretrained:
            if isinstance(weights_path, str):
                state_dict = torch.load(weights_path)
            else:
                from torch.utils import model_zoo

                # down pretrained models
                state_dict = model_zoo.load_url(url_map["videoswin3D"])

            ret = model.load_state_dict(state_dict, strict=False)
            assert (
                not ret.missing_keys
            ), "Missing keys when loading pretrained weights: {}".format(
                ret.missing_keys
            )

        return model

    def forward(self, x):
        x = self.patch_embed(x)

        x = self.pos_drop(x)

        for i, layer in enumerate(self.layers):
            x = layer(x.contiguous())

        x = x.permute(0, 2, 3, 4, 1)
        x = self.norm(x)
        x = x.permute(0, 4, 1, 2, 3)

        return x


if __name__ == "__main__":
    enc = SwinTransformer3D.from_pretrained(pretrained=False)
    img = torch.randn(2, 6, 3, 448, 448)
    _B, _T, _C, _H, _W = img.shape
    _h, _w = _H // 32, _W // 32
    img_feat = enc(img.transpose(1, 2)).transpose(1, 2)  # [_B, _T, 768, _h,  _w]
